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A new quality assessment for Thangka image inpainting
Author(s) -
Hu Wenjin,
Liu Zhongmin,
Ye Yuqi
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4671
Subject(s) - inpainting , artificial intelligence , computer science , consistency (knowledge bases) , computer vision , image (mathematics) , metric (unit) , image restoration , pattern recognition (psychology) , image quality , enhanced data rates for gsm evolution , image texture , component (thermodynamics) , quality (philosophy) , image processing , engineering , operations management , physics , epistemology , thermodynamics , philosophy
Summary This paper presents an efficient metric for evaluation the effect of the inpainted Thangka images. In contrast to standard image quality metrics, the proposed one takes into account some constraints and characteristics related to the specific goals of inpainting techniques. The key is that we proposed a method to decompose the reference and distorted image and to compare the intensity of fuzzy edge for structure component and similarity for texture component. By comparative analyzing the experimental results with other metrics, the proposed image inpainting quality index delivers high consistency with subjective evaluation results, which will help the selecting algorithm in image restoration and could be also applied to most of inpainting image approaches.